Exploring Continual, Distributed, Secure & Explainable AI.
Adapt AI models over time without forgetting past knowledge.
Leverage distributed systems for scalable, collaborative learning across devices.
Protect data and models with robust security and privacy mechanisms.
Develop interpretable AI systems that provide clear insights into decision making.
I am a PhD candidate in Computer Engineering at Colorado State University, specializing in artificial intelligence and machine learning systems. My research focuses on building scalable, adaptive and robust models that operate efficiently in real‑world conditions. I work at the intersection of continual learning, secure and federated ML, explainable AI and multimodal perception, with applications such as indoor localization, object detection and large language model robustness.
This page shares my research interests, publications, professional experiences, projects and ways to get in touch. Feel free to explore the tabs above to learn more!
My research aims to develop machine learning systems that are adaptable, robust and energy‑efficient. I investigate continual and domain‑incremental learning methods, robust and secure ML techniques, federated and distributed learning algorithms, multimodal perception systems and explainable AI (XAI) approaches. By combining systems and algorithmic perspectives, I build models that generalize across different devices, domains and over time, while maintaining high performance under constraints like limited data, hardware resources and adversarial conditions. A recent focus of my work is on XAI techniques that provide transparency and interpretability in model decisions.
Some of my current interests include:
Email: akhil.singampalli@colostate.edu
Location: Fort Collins, CO, United States
If you would like to collaborate or learn more about my research, feel free to reach out via email.